This section provides background information related to the present disclosure which is not necessarily prior art.
The desire to improve quality has spread to nearly all manufacturing and service industries and businesses. There is a great interest in improving quality of products and services through systematic performance evaluation followed by business process improvement. Some industries are fortunate to have easily quantifiable metrics to measure the quality of their products or services. Using these metrics, a continuous improvement process can be implemented, whereby the product or service is produced using existing processes and assessed through quantifiable metrics, the existing processes are then changed based on the results of the metrics, and the efficacy of the change is tested by producing the product or service again using the changed process.
For most industries, however, finding a good, quantifiable metric has proven difficult, as business process have become quite complex and difficult to describe in quantifiable measures. Human intuition and judgment play an important role in production of goods and services, and ultimately, human satisfaction plays the decisive role in determining which goods and services sell well and which do not.
Human intuition and judgment and customer satisfaction are intangible variables that are not directly measurable and must therefore be inferred from data that are measurable. Therein lies the root of a major problem in applying continuous improvement techniques to achieve better quality. The data needed to improve quality are hidden, often deeply within reams of data that an organization generates for other purposes. Even surveys expressly designed to uncover this hidden data can frequently fail to produce meaningful results unless the data are well understood and closely monitored.
Experts in statistical analysis know to represent such intangible variables as latent variables that are derived from measurable variables known as manifest variables. Even experts in statistical analysis, however, cannot say that manifest variable A will always measure latent variable B. The relationship is rarely that direct. More frequently, the relationship between manifest variable A and latent variable B involves a hypothesis, which must be carefully tested through significant statistical analysis before being relied upon.
U.S. Pat. No. 6,192,319 discloses a statistical impact analysis system that uses a Partial Least Squares (PLS) software module to determine the relationships between manifest variables and latent variables. Traditional PLS algorithms, such as Latent Variable PLS (LV-PLS), are used to estimate the case values of the latent variables as the linear combinations of their manifest variables. In the existing LV-PLS algorithm, the PLS weights are determined in such a way that the specified predictive relationships explain the data. This data driven approach is appropriate when the research objective is to find the model structure best fitting a given data set. Current LV-PLS modules, however, do not allow a user to specify the strategic priorities of the performance measures so that the calculations of the predictor latent variables are tuned to better predict the performance measures with higher priorities. Thus, when assessing a product, service or business, it would be advantageous to be able to fine tune an impact analysis machine to predict the performance measures with higher priorities.
Furthermore, traditional PLS regression methods based upon NIPALS have two critical limitations. First, when predictor variables, that is, latent variables used to predict values of dependent latent variables, are highly correlated, the first component explains most of the variation in the response variable, and only a small portion is left to be explained by the subsequent components. Therefore, traditional PLS models result in only one or two components being used for prediction. The second limitation is that the current models do not have an effective way of constraining the coefficients used in the regression.